Mid speed scada with DFE equalizer

By Miz.Wong, 2018

Miz.Wang@Hytera.com

Hytera Co,. Ltd.

System Info

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Symbol Rate 10ksym/s

Band width 12.5kHz

Modulation type 16-QAM

Real information rate 35kBit/s

DFE taps 1 Forward 1 Backward

DFE forgetting factory 0.99

Frame structure |Training sequence|Information|

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1.Clean up workspace

clear all;

close all;

clc;

2.Set system parameters

M = 16;

Rs = 10e3;

Rb = Rs * log2(M);

BB_OSR = 8;

FsBB = Rs * BB_OSR;

IF_OSR = 36;

FsIF = FsBB * IF_OSR;

Coefs = load('FilterCoefs.mat');

coefRRC = Coefs.coefRRC;

coefIF = Coefs.coefIF;

hMod = comm.RectangularQAMModulator( M, 'BitInput', true );

hDem = comm.RectangularQAMDemodulator( M, 'BitOutput', true );

T = 0.04;

nTSHead = 50;

tBB = 0 : 1/FsBB : T - 1/FsBB;

tIF = 0 : 1/FsIF : T - 1/FsIF;

txcPPM = 10;

txcFreq = 100e3;

txcFreq = txcFreq + (txcFreq / 1e6)*txcPPM;

txcPhi0 = 0;

rxcFreq = 100e3;

rxcPhi0 = pi/3;

fd = 2;

pdb = [0 -22.3];

tau = [0 5e-6];

RlChan = rayleighchan( (1 / FsIF), fd, tau, pdb);

hEVM = comm.EVM( 'ReferenceSignalSource', 'Estimated from reference constellation', ...

'ReferenceConstellation', constellation(hMod).' );

hMyDFE = fnDFECreate(1, 1, 0.9, hMod, hDem);

3.Transmitter

txBit = randi( [0,1], T*Rb, 1 );

txSym = step(hMod, txBit );

txSymUp = zeros( length(txSym)*BB_OSR, 1 );

txSymUp(1 : BB_OSR : end) = txSym;

txSymUp = txSymUp * BB_OSR;

txSymUp = [zeros(200,1); txSymUp; zeros(200,1);];

txSymUp = FilterConv( txSymUp.', coefRRC' ).' ;

txSymIF = zeros( length(txSymUp)*IF_OSR, 1 );

txSymIF(1 : IF_OSR : end) = txSymUp;

txSymIF = txSymIF * IF_OSR;

txSymIF = FilterConv( txSymIF.', coefIF' ).' ;

tIF = 0 : 1/FsIF : length(txSymUp)/FsBB - 1/FsIF;

txSymIF = txSymIF .* exp( 1j*( 2*pi*txcFreq*tIF' + txcPhi0 ) );

figure; plot(txSym, 'o-'); title('TX symbols');

hs = spectrum.periodogram; figure;psd(hs, txSymIF, 'Fs',FsIF, 'CenterDC',true);

4.Channel

rxSymIF = txSymIF;

rxSymIF = filter( RlChan, rxSymIF );

rxSymIF = awgn( rxSymIF, 0, 'measured' );

hs = spectrum.periodogram; figure;psd(hs, rxSymIF, 'Fs',FsIF, 'CenterDC',true);

5.Receiver

5.1 DDC & RRC

rxSymIF = rxSymIF ./ exp( 1j*( 2*pi*rxcFreq*tIF' + rxcPhi0 ) );

rxSymIF = FilterConv( rxSymIF.', coefIF' ).' ;

rxSymUp = zeros( length(rxSymIF) / IF_OSR, 1 );

rxSymUp = rxSymIF( 1 : IF_OSR : end );

rxSymUp = FilterConv( rxSymUp.', coefRRC' ).';

figure; plot(rxSymUp, '.'); title('Pre-VSR');

5.2 Symbol synchronization & Downsampling

for ii = 1:1:BB_OSR

avePwr(ii) = mean(abs(rxSymUp(ii:BB_OSR:end)));

end

[max T0] = max(avePwr);

rxSymUp = rxSymUp(201:end-200);

rxSym = rxSymUp(T0:BB_OSR:end);

fprintf( 'T0 = %d\r\n', T0 );

T0 = 1

figure; plot(rxSym,'.'); title('Pre-DFE');

5.3 Decision Feedback Equalization (DFE)

[hMyDFE, rxSymDFE, vyDcs] = fnDFE( hMyDFE, rxSym, [1:nTSHead], txSym );

figure; plot(rxSymDFE(nTSHead+1:end),'.'); title('Post-DFE');

6.DFE performance

BitNeq = step(hDem, rxSym);

BERDFE = (sum( vyDcs(nTSHead*log2(M)+1:end)' ~= txBit(nTSHead*log2(M)+1:end) ) / length(txBit(nTSHead*log2(M)+1:end))) * 100;

BERORG = (sum( BitNeq(nTSHead*log2(M)+1:end) ~= txBit(nTSHead*log2(M)+1:end) ) / length(txBit(nTSHead*log2(M)+1:end))) * 100;

fprintf( 'BER : \r\nWithout DFE : %f%%\r\nWith DFE : %f%%\r\n', BERORG, BERDFE );

BER :
Without DFE : 49.357143%
With DFE : 29.000000%

EVMDFE = hEVM(rxSymDFE(nTSHead+1:end));

EVMORG = hEVM(rxSym(nTSHead+1:end));

fprintf( 'EVM : \r\nWithout DFE : %f%%\r\nWith DFE : %f%%\r\n', EVMORG, EVMDFE );

EVM :
Without DFE : 83.915749%
With DFE : 29.608875%

figure; stem(real(hMyDFE.e));hold on;stem(imag(hMyDFE.e));title('Equalizer error');

figure; plot(abs(txSym./rxSym),'x-');hold on;plot(abs(txSym./rxSymDFE),'o-');

title('Magnitude error without and with DFE');

figure; plot(phase(txSym./rxSym),'x-');hold on;plot(phase(txSym./rxSymDFE),'o-');

title('Phase error without and with DFE');

Performance of BER

This figure shows the BER performance in different SNR :

More about DFE

DFE is a decision feedback structure, shown below:

Currently, my code only support symbol-space DFE, so K is always 1.

'Weight setting' part can be LMS, RLS, Kalman and other algorithms. Currently, only RLS is supported.

The core formula of RLS is :

K is kalman gain matrix, P is inverse correlation matrix, u is the input vector, e is the error vector, lamda is fogetting factory, H denotes the Hermitian transpose, * denotes the complex conjugate.